Title 



Predicting current user intent with contextual markov models
 
Author 



 
Abstract 



In many web information systems like eshops and information portals predictive modeling is used to understand user intentions based on their browsing behavior. User behavior is inherently sensitive to various contexts. Identifying such relevant contexts can help to improve the prediction performance. In this work, we propose a formal approach in which the context discovery process is defined as an optimization problem. For simplicity we assume a concrete yet generic scenario in which context is considered to be a secondary label of an instance that is either known from the available contextual attribute (e.g. user location) or can be induced from the training data (e.g. novice vs. expert user). In an ideal case, the objective function of the optimization problem has an analytical form enabling us to design a context discovery algorithm solving the optimization problem directly. An example with Markov models, a typical approach for modeling user browsing behavior, shows that the derived analytical form of the optimization problem provides us with useful mathematical insights of the problem. Experiments with a realworld usecase show that we can discover useful contexts allowing us to significantly improve the prediction of user intentions with contextual Markov models.   
Language 



English
 
Source (book) 



IEEE 13th International Conference on Data Mining (ICDM), December 0710, 2013, Dallas, Texas  
Publication 



New York, N.Y. : IEEE, 2013
 
ISBN 



9780769551098
 
Volume/pages 



(2013), p. 391398
 
ISI 



000343602800052
 
Full text (Publisher's DOI) 


  
